LEADER 03901nam 22006975 450 001 9910645887003321 005 20230519121531.0 010 $a9783031130052$b(electronic bk.) 010 $z9783031130045 024 7 $a10.1007/978-3-031-13005-2 035 $a(MiAaPQ)EBC7184755 035 $a(Au-PeEL)EBL7184755 035 $a(CKB)26037405400041 035 $a(MiAaPQ)EBC7184753 035 $a(DE-He213)978-3-031-13005-2 035 $a(PPN)26780752X 035 $a(EXLCZ)9926037405400041 100 $a20230120d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Multivariate Statistics with R /$fby Daniel Zelterman 205 $a2nd ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (469 pages) 225 1 $aStatistics for Biology and Health,$x2197-5671 300 $aIncludes index. 311 08$aPrint version: Zelterman, Daniel Applied Multivariate Statistics with R Cham : Springer International Publishing AG,c2023 9783031130045 327 $aChapter 1. Introduction -- Chapter 2. Elements of R -- Chapter 3. Graphical Displays -- Chapter 4. Basic Linear Algebra -- Chapter 5. The Univariate Normal Distribution -- Chapter 6. Bivariate Normal Distribution -- Chapter 7. Multivariate Normal Distribution -- Chapter 8. Factor Methods -- Chapter 9. Multivariate Linear Regression -- Chapter 10. Discrimination and Classification -- Chapter 11. Clustering Methods -- Chapter 12. Basic Models for Longitudinal Data -- Chapter 13. Time Series Models -- Chapter 14. Other Useful Methods. 330 $aNow in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R, Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. He uses practical examples from diverse disciplines, to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The book avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an excellent resource for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work. 410 0$aStatistics for Biology and Health,$x2197-5671 606 $aBiometry 606 $aBioinformatics 606 $aEpidemiology 606 $aBiostatistics 606 $aBioinformatics 606 $aEpidemiology 606 $aAnàlisi multivariable$2thub 606 $aProcessament de dades$2thub 606 $aR (Llenguatge de programació)$2thub 608 $aLlibres electrònics$2thub 615 0$aBiometry. 615 0$aBioinformatics. 615 0$aEpidemiology. 615 14$aBiostatistics. 615 24$aBioinformatics. 615 24$aEpidemiology. 615 7$aAnàlisi multivariable 615 7$aProcessament de dades 615 7$aR (Llenguatge de programació) 676 $a570.285 676 $a519.53502855133 700 $aZelterman$b Daniel$0144977 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910645887003321 996 $aApplied multivariate statistics with R$91522551 997 $aUNINA